Research @ Infosys Labstag:www.infosysblogs.com,2010-03-19:/infosys-labs//312018-12-12T09:25:59ZThe Infosys Labs research blog tracks trends in technology with a focus on applied research in Information and Communication Technology (ICT)Movable Type 5.14-enResetting Robot's Dreamtag:www.infosysblogs.com,2018:/infosys-labs//31.108162018-12-12T09:17:16Z2018-12-12T09:25:59Z"Cal is a helper house-Robot owned by Mr. Northrop, an author and technology enthusiast. Mr. Northrop is a prolific writer and sometimes loses track of other activities, he likes the way Cal picks up after him, runs his printer, stacks...Jyotirmay Ray
"Cal is a helper house-Robot owned by Mr. Northrop, an
author and technology enthusiast. Mr. Northrop is a prolific writer and
sometimes loses track of other activities, he likes the way Cal picks up after
him, runs his printer, stacks his disks, and other things. He doesn't need a
complicated robot and Cal surely fits in. But Cal is a special robot with a level
of intelligence not completely explored and with time Cal develops curiosity
and interest in writing. More like being influenced by the author persona of
his master. As Mr. Northrop comes to know Cal's interest he decides to upgrade
Cal with dictionary, vocabulary, grammar, and other essentials for writing
stuff. Cal starts writing, initially he wrote random letters like gibberish.
But with more upgrades and advice from Mr. Northrop, Cal got better and better.
After few attempts Cal wrote a satire with perfect sense of the ridiculous, Mr.
Northrop read the story 2-3 times; a sudden feeling of insecurity came to him,
what if Cal writes more stories and continues to improve each time? Mr.
Northrop decided to undo all improvements and reset Cal as it was when he
bought. "

Above is the summary of science fiction short story written by
Isaac Asimov
in 1991. He wrote many stories on robotics and often credited with devising
Three laws of Robotics, which was adapted into Hollywood sci-fiaction film "I,
Robot" starring Will Smith.

The vision on future of robotic automation and questions raised
by Asimov on freedom of choice is even more relevant in era growing practice of
AI. The core issue, that may have prompted Mr. Northrop to take the reset
route, is his inability to appreciate the robots did and the grey area around robots
decision making which is incomprehensible. Recently Facebook was experimenting
with chatbots which were to negotiate among each other for ownership of virtual
items, but after a few rounds the AI programs seemed to be interacting in a language
that only they understood; Facebook had to shut down the experiment.

Transparency is a major factor that we need to address for
building sustainable AI systems, in above case had Mr. Northrop knew that Cal
was only trying to mimic him for extending help rather than being a competition,
his action could have been different. Along with that interpretability and explainability
of decision taken by AI systems would nullify grey areas, thereby building
confidence among user community on trustworthiness of the systems. The factors
will be crucial as organizationssail
through the transformation journey of industry 4.0 where AI will have
significant penetration across industry verticals.

To stay ahead
with the AI curve, Organizations must build trust in their AI application. That
will also speed up adoption of AI application among the stakeholders within and
outside the organizations. For example, there is huge potential for AI in
banking sector. In areas like traditional loan approval value chain from
application to disbursement, AI can be applied at stages such as validation,
due diligence, and approval; but lack of trust & transparency in AI applications
hinders the adoption of AI led loan evaluation process. There are many such
cases across industries like customer recommended in retail, optimizing the
distribution of energy, fraudulent reimbursement in insurance etc.

Moving on to digital era we will be surrounded
smart AI systems and would interacting with real life CAL s for day-in day-out.
So, it's our imperative to build robust mechanism for explainability as well as
trusted and sustainable AI systems.

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Rise of Emotional Intelligence in AItag:www.infosysblogs.com,2018:/infosys-labs//31.108122018-12-07T06:31:21Z2018-12-07T06:34:54ZWe typically prefer to be with people who can understand us and are emotionally intelligent. Body language and tone play a significant part in what we think and feel. Emotional intelligence encompasses the ability of people to recognize, understand and...Rohit Chopra
We typically prefer to be with people who can understand us
and are emotionally intelligent. Body language and tone play a significant part
in what we think and feel. Emotional intelligence encompasses the ability of
people to recognize, understand and control their own emotions as well as
recognize, understand and influence others' emotions. EQ has become an
important consideration when we talk about AI development. As per Rana el
Kaliouby, co founder and CEO of Affectiva, an MIT spinout company that works on
emotional recognition technology, "If it's interfacing with a human, it needs
social and emotional skills." The addition of EQ to AI will help such systems
respond better to more complex human needs leading to creation of better
customer experiences and thereby improve customer satisfaction.

Businesses are increasingly benefitting from advances in
emotionally intelligent AI as they uncover new opportunities by understanding consumer
likes and dislikes along with gauging their affinity towards a brand or product.
As per a recent study by Market Research Future (MRFR), the global emotion
analytics market is expected to reach USD 25 billion by 2023, growing at a CAGR
of 17% between 2017 and 2023. Also, Gartner predicts that by 2022, 10% of our
personal devices will include emotional AI capabilities, up from less than 1%
in 2018. Using sentiment analysis to understand consumer perception towards a
product/brand in the offline world has remained a daunting task. Detecting
emotions from facial expressions using AI can be used as a substitute to better
understand consumer preferences and how they engage with particular brands.

Traditionally market research companies have relied on using
different methods such a surveys, trade interviews to better understand consumer
requirements. However, these methods assume a direct correlation between future
actions and what the consumers state verbally, which may not always be accurate.
In this scenario, behavioral methods are considered more objective and are
often deployed to observe a user's reaction while interacting with a
product/brand. Manually analyzing video feeds of users interacting with a
product/brand can be pretty labor intensive. Facial emotion recognition can be
useful in this scenario as they allow market research companies to record
facial expressions automatically and derive meaningful insights from them.

Disney has designed an AI-powered algorithm to gain a better
understanding of how audiences enjoy its movies, this algorithm can recognize
complex facial expressions and also predict how audiences will react for the
remaining part of the movie. As per reports, the tests processed a staggering
figure of 16 million data points derived from 3,179 viewers.

Earlier this year, Soul Machines partnered with Daimler
Financial Services to present "Sarah", a digital human as an interface to
Daimler's financial services and mobility ecosystem aiding them to deliver enhanced
customer experiences in the areas of car financing, leasing and insurance by
utilizing facial gestures and natural voice intonation.

Annette Zimmermann, vice president of research at Gartner
claimed in January 2018, "By 2022, your personal device will know more about
your emotional state than your own family." Facial analysis, voice pattern
analysis and deep learning when used together in conjunction can help decipher
human emotions with applications across a broad range of industries such as retail,
financial services, medical diagnosis, autonomous cars, fraud detection and recruitment
among others.

The shift from data-driven interactions relying heavily on
IQ to EQ-guided experiences will also present companies an opportunity to
connect with customers on a much more intimate level. However, emotions are immensely
personal and companies working in this space should be wary about consumer
concerns such as intrusion of personal space and manipulation. Suitable
psychological training for people is also required to interpret emotional
results from these machines and fix deviations as deemed appropriate.

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Explainable AI - Introduction and applicationstag:www.infosysblogs.com,2018:/infosys-labs//31.108112018-11-30T08:18:12Z2018-11-30T08:30:06ZAI systems have essentially remained black boxes, with deep learning models frequently remaining opaque. It has become imperative to build systems which can justify their decisions, very similar to how humans operate. Significant advances in this area will result in...Rohit Chopra
AI systems have essentially remained black boxes, with deep
learning models frequently remaining opaque. It has become imperative to build
systems which can justify their decisions, very similar to how humans operate. Significant
advances in this area will result in the evolution of autonomous systems that are
able to learn, make decisions and implement them without the support of any
external agents. Explainable AI (XAI) is artificial intelligence that is
programmed to describe its purpose, rationale and decision-making process in a
way that can be understood by the average person. Powerful algorithms often churn
out useful results, without explaining how they arrived at it. Thus, transparency
is often compromised while arriving at sophisticated experimental results using
AI systems. As AI models become more complex, it is important for these systems
to provide verifiable explanations of the decisions they make. Key benefits
derived from the implementation of XAI include:

·Aid in faster and broader deployment of AI

·Bring convenience and speed to consumers along
with building trust

·Adoption of best practices around the areas of
compliance, accountability and ethics

·Reduce impact of biased algorithms

The figure below illustrates the concept of XAI as
demonstrated by Defense Advanced Research Projects Agency (DARPA):

Source: XAI Concept by DARPA

AI systems have multiple applications across industries. For
example, in the financial services domain it will be important for AI systems
to be able to explain their decision making in order to be fully embraced and
gain trust in the industry. If a loan application process is denied by an
automated system powered by AI, bank executives should be able to trace the
decision to the specific step where the denial occurred and also provide a
reasoning for the AI system's decision at that particular step.

An AI system which is determining the premium charges for
car insurance should also be able to provide the rationale behind such a decision
based on several factors including age, gender, car type, accident history,
address, mileage etc. It should also aid in providing a personalized experience
by mentioning what the customer needs to do in order to reduce premium charges,
for example drive accident free for the next one year.

An ethical risk is also prevalent in this scenario as bias
can unintentionally creep into algorithmic models and thereby result in
discriminatory practices. This puts organizations at risk as consumers are
likely to switch brands once they understand about these prejudices. For
example, certain existing AI algorithms imposed higher charges for Asian
Americans opting for SAT tutoring. Facial recognition software is being
increasingly used for law enforcement and is also promulgating racial and
gender bias. Earlier this year, Joy Buoalamwini from the Massachusetts Institute
of Technology showed that gender-recognition AIS from IBM, Microsoft and
Chinese company Megvii were able to identify gender from a photograph for white
men with an accuracy of 99%. However, this number was staggeringly low at 35%
for dark-skinned women. This poses increased risk towards false identification
of women and minorities.

Explainable AI will thus help to build models which can
identify relevant stakeholders and the information they require about how the
model arrives at decisions. This would also identify any form of bias which has
crept in and aid data scientists weed them out at an early stage. Eventually as
humans and machines work together more effectively, it will be imperative for us
to understand the machine logic lying underneath.

Transparency will become an important requirement to keep up
with compliance regulations. For example, the General Data Protection
Regulation (GDPR) with a focus on right to explanation mandates that users
should be able to demand data behind algorithmic decisions made by recommendation
engines. This puts the onus on companies to translate complicated reasoning
behind AI algorithms to simple and easily interpretable language.

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Rise of Digital banks!tag:www.infosysblogs.com,2018:/infosys-labs//31.107762018-09-30T22:54:00Z2018-09-30T18:51:40ZThis blog covers the trend of digital banking and a overall view of the direction truly digital banks are takingAkanksha Rajendra Singh

Our computers have become windows through which we can gaze upon a world that is virtually without horizons or boundaries.

~ Joseph B. Wirthlin

Ever complained about standing in queues and having to sign countless banking forms? Ever wondered why you need to walk into a branch of your bank to perform mundane tasks which can be easily done with a tap on your smartphone? If you have, then you are not alone. Welcome to the new world of banking where several startups and even some traditional banking giants are experimenting with all online and fully functional digital banks. These digital banks are industriously working towards addressing many of the consumer pain points in the business as usual banking world.

Digital-only banks are on the rise. And by digital-only banks, I am referring to truly digital banks who do not have any brick and mortar presence. They have an edge over traditional banks in terms of efficiency, speed, ROI and scale. Being fully automated, they are more efficient than their traditional counterparts where still a lot of processing is manual. Digital banks deliver services faster than a brick and mortar business while being efficient at the same time.

Setting up a digital bank is again more pocket friendly when compared to a regular bank. Also, a digital bank can be scaled up to meet rising consumer demands much more easily. Another major advantage is in terms of readily available data. Digital banks can undertake AI/ML initiatives with much more ease to deliver faster insights and implement changes with reduced time to market. Data cleansing and curation process becomes seamless.

Digital banks are very much powered by technology and are at the forefront of experimenting with all the cutting-edge advances available. AI powered virtual assistants, digital only cash, peer-to-peer lending and payments and blockchain based banking transactions have all been tried out in digital banking arena. Touch ID serves as an efficient security tool which provides a safe way to login to banking accounts.

Digital banks come in all flavors. Some banks offer zero maintenance fees. Few others offer rewards based on your social media likes. Revoult, a digital-only bank, offers global payments and cryptocurrency exchange. Yet another section of banks cater to a very niche segment of consumers. For example, USAA is a member only bank serving US military members and their kin.

Banking is at the cusp of a digital revolution similar to what the retail industry witnessed few years ago. Amazon disrupted brick and mortal retail stores with an all-online market. Likewise, digital banks are all set to disrupt traditional banks. Agreed there are rigid regulations and compliance in place which digital banks will have to adhere to; yet they are flourishing at a super fast pace. Banking as a platform or banking as a service may become the norm in banking sooner than later.

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Sports and Technologytag:www.infosysblogs.com,2018:/infosys-labs//31.107692018-09-26T12:42:16Z2018-09-26T12:44:29ZThis blog explores how sports leagues, associations and teams are embracing technology to engage their fans ain the digital ageEnoch Sarin Paul
Technology has made its way into aspect of our life. It has
changed the way we work, travel and live. During this apparent transformation
sports, an aspect of our lives enjoyed by all has been leveraging technology to
enhance their performance and reach out to their loyal fans.

The introduction of television into our lives was a game
changer for sports. TV's enables viewers to watch the game from the comfort of
their homes and follow their favorite teams no matter where they played. It
also enabled fans to access and learn the various sports played around the
world. Whether it was sports like soccer or F1, television increased their fan
base like no other technology had ever before. New revenue streams were created
for the sports teams and associations through the sale of broadcasting rights.

Many sports have also embraced technologies for providing replays,
to review umpire decisions and such predict the direction of the ball. The
Hawk-Eye system used to predict the direction of the ball has already been
embraced by various sports such as tennis and cricket.

But, a new wave of technology is promising to revolutionize
the way we enjoy our sports. Startups are designing jerseys which the fan can
wear to feel the intensity of the game through haptic feedback which is
generated by the adrenaline and excitement of their favorite NFL team. The
technology brings the feel of a stadium to fans watching the game from home.
Another such application used the NFL is Be the Player, the application allows fans
to watch the game from the point of view of their favorite player without
having the player wear a camera.

Sports Associations are revolutionizing fan engagement by
leveraging social media sites and virtual games. Fans can now get personalized
feed of the NFL games enabling fans to select players they want to follow and
watch off the field clips of the team in the locker room and post win parties.
The NBA is engaging fans on the internet by counting votes through social
media, google, etc. to select players for the All-Stars game. They even have a
chatbot which can show clips of players or matches based on requests from fans.

Virtual reality is another aspect being embraced by technology.
Games are now being telecast for fans to watch through VR in order to provide a
more immersive experience for remotely viewing fans.

The emergence of new technology and the demand for higher
levels of engagement from fans are forcing sports teams and associations to
search and identify new channels of engagement. With the rapid rate of adoption
of technology, it won't be long before we will be able to immerse ourselves in
the excitement of the game and be closer to our favorite players than ever before.

https://www.business2community.com/sports/how-the-nba-is-on-the-ball-with-customer-engagement-0131836/amp ]]>
Artificial Intelligence & Financial Services - The Applicationstag:www.infosysblogs.com,2018:/infosys-labs//31.107682018-09-26T12:39:00Z2018-09-26T12:41:29ZIn this blog we explore the applications of AI in the front, middle and back office of financial institutions and how some organizations have embraced these applications.Enoch Sarin Paul
In my previous blog "Artificial Intelligence & Financial
Services - The Business Case" we explored the business case for adoption of AI
in the financial industry. In this blog we will look at the technology, its
applications and its adoption by the industry.

The most mature use cases are in chatbots in the front
office, antifraud and risk and KYC/AML in the middle office, and credit
underwriting in the back office.

Front office operations have leveraged chatbots to
revolutionize customer relationship management. Other than assisting customers
with their transactions, chatbots enable banks to segment customers
individually rather than general buckets by collecting data regarding their
behavior and habits. Infact, Nina, Swedbank's AI chatbot was deployed to assist
customers 2 years ago. Already, Nina has successfully demonstrated its ability
to resolve 78% contact resolution and has a customer adoption rate of 30,000
conversations per month. By 2024, 42.82% of the estimated $1.25 billion market
for chatbots is expected to be generated by the rising need for enhancing the
customer services to retain existing customers and attracting potential
customers.

Similarly, AI based anti-fraud and KYC/AML applications have
been gaining traction in middle office operations thanks to the superior
cognitive capabilities of AI. The digitization of banking products and services
has led to an increased susceptibility to fraud. Using AI significantly reduces
the time taken to review transaction, including all the factors and relevant
data associated with it. Lloyds Banking Group is using AI models that detect
when the person logged in is not the customer, but a fraudster or a bot.
Similarly, Natwest has been using AI to reduce fraudulent transactions and
reported that AI has prevented 7 million Pounds of false payments.

Back office operations too are undergoing a change. AI is
used for applications such as credit and risk underwriting in the back office
by creating a more complete and unbiased assessment of an applicant's credit
worthiness. AI based underwriting provides a more comprehensive view of the assessee'
s credit worthiness. Startup, Lenddo has already enabled their partners to
assess 5 million applicants through the 12,000 variables collected from
alternative sources.

AI has also been widely adopted by hedge funds for
algorithmic trading, AI collects data from several sources to create a more
accurate prediction. They are also being used by banks to discover investment
opportunities by scouring the markets. CircleUp, a venture capital firm has
created Classifier, a machine learning crowdfunding platform to determine which
companies to fund. The Classifier has the capability to review 500
opportunities per month with a team of less than 10 analysts vs the 500
evaluations per year done by the average private equity firm.

While chatbots, anti-fraud, risk management, KYC/AML, credit
underwriting and asset management are being adopted by the financial industry,
the other applications are generating a lot of interest and it won't be long
before they too go mainstream. With AI estimated to add more than 1 trillion in
value to the financial industry it won't be long before robots cater to our
financial needs while algorithms manage our daily finances.

https://www.techemergence.com/artificial-intelligence-applications-lending-loan-management/ ]]>
Artificial Intelligence & Financial Services - The Business Casetag:www.infosysblogs.com,2018:/infosys-labs//31.107672018-09-26T12:28:28Z2018-09-26T12:46:15ZIn this blog we explore the business case for AI in the financial industry and assess the impact of certain technologies on the front, middle and back officeEnoch Sarin PaulArtificial Intelligence & Financial Services - The Business Case

The new wave of innovation and technology commonly referred
to as "fintech" is reshaping the financial services industry and forcing the
critical financial intermediaries to adopt emerging technologies. AI has been
the focus of financial institutions due to the opportunities arising from the rise
in volume of data, speed of access to it and the emergence of new and advanced
algorithms able to analyse data in a more intelligent. Industries are
leveraging the technology to gain a competitive advantage against their peers
by improving speed, cost efficiency and accuracy of processes and meeting rising
customer expectations.

The highest adoption rates of AI in financial services
companies are in IT with 63.5%, finance and accounting with 40.4%, marketing
with 31.4% and customer services with 30.8%. Challenges with the adoption of AI
in the financial service industry has been the auditability and traceability of
the applications. Financial institutions have to comply with regulations requiring
them to explain their decisions to customers and report the same to regulators.

Cost savings is expected to be the primary driver of AI in
the financial industry, analysts estimate that AI will save the banking
Industry $1 trillion in savings by 2035 with most of the savings coming from
the front office. Although the changes are predicted to be gradual until 2025,
the adoption rate is expected to accelerate until 2030.

Reduction in the scale of retail branch networks and other
distribution staff will generate most of the savings in the front office with
$199 billion. Chatbots are expected to take over and handle upto 85% of the
world's customer interaction. Chatbots are already being leveraged to help
customers manage their personal finances, provide investment advice and suggest
the best product for the customer while enabling the customer to perform simple
transactions effortlessly.

The application of AI for compliance, KYC/AML and data
processing is forecasted to save $217 billion in the middle office. One of the
mature applications, KYC/AML uses pattern detection d unstructured text
analysis to identify potential fraudulent activity in real time while identifying
complex linkages between entities.

Back office operations are also expected to generate $200
billion in savings of which $31 billion will be generated through the application
of AI for underwriting and collection systems. AI enables underwriters to
collect data from alternative sources such as social media and geolocation data
enabling to assess candidates with limited credit history and speed up the
entire process.

Applications of AI range from customer service through AI
assistants to process automation tools for eliminating time intensive work. Irrespective
of whether its intelligent automation for repetitive manual tasks, the enhanced
judgement and improved interactions provided by AI is the future of the
industry and will drive enterprise growth and profitability in the years to
come.

In my next blog "Artificial Intelligence & Financial
Services - The Applications", we will dive deeper into the application of AI in
financial services.

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Journey towards Adaptive Caretag:www.infosysblogs.com,2018:/infosys-labs//31.107662018-09-26T03:56:44Z2018-09-26T04:06:16ZHistorically healthcare has been intermittent and reactive in nature. Even in today's world of digital, mobile, and technological breakthroughs (both medical sciences and ICT) when it comes to personal care people tends to follow a reactive to disease approach. That...Jyotirmay Ray
Historically healthcare has been intermittent and reactive in nature. Even in today's world of digital, mobile, and technological breakthroughs (both medical sciences and ICT) when it comes to personal care people tends to follow a reactive to disease approach. That might be suitable for a sick care scenario but journey towards continuous and proactive healthcare will require a more connected environment, personalization, better patient experience, home care, and predictive medications. Rather than relying on intermittent data for diagnostics decision, patient should be at the center of care system which will give a complete view of the patient's biological, physical, mental status.

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Technology Talk

We are living in an era of digital transformation where technological innovation is occurring at an exponential rate. Healthcare is not devoid of it, in last few years' adoptability and usage of wearable bands and devices, smart watches, biometric sensors, insidables, IoT sensors, smart mirrors, AR/VR technologies for healthcare related application has increased significantly. As per survey conducted by Accenture (consumer survey on digital health):

More and more people are willing to use technology for immediate and virtual care services. But, lack of interoperability, continuous data, and common platform makes the devices function in isolation. So today the calorie data by fitbit, and body temp by a biometric sensor gets captured, analyzed, and consumed separately. An integrated system seamlessly leveraging all the user devices would lay the foundation of adaptive care and enable us for next gen technologies around genomics, AI led decision support, nano-tech, brain computer interface, 3D printed tissues & organs, precision medicine etc.

Adaptive care

An adaptive care system would combine intelligent agents, smart devices, human actions with cognitive algorithms to optimally sense and respond to changes in parameters and suggest best course of action. It can be envisaged as a home or patient care facility equipped with wearable bands and devices, smart watches, biometric sensors, insideables, IoT sensors, smart mirrors, connected medical devices, AR/VR to create a network with seamless user interaction with surroundings. Broadly it would serve

Lifestyle care: Recommendation of Food, level of waters and liquids

Fitness care: Setting up workout routine, Tracking Calories

Personal Care: Maintenance of Hygiene, Remote monitoring

Medical Care: Alert on Spikes & Doctor Appointment, Virtual Care

The adaptive system will work on the principal of Sense, Analyze, and Response. Wherein the response would vary from person to person based on to the network inputs, current state of patient, and historical trends. As system learns more about the patient it will provide more and more personalized recommendation and suggestions. For example, normal body temperate varies for different persons, 99F can be normal for person A, whereas 97F for person B; so if morning body temp touches 100F, system's response would be different for person A and B. The adaptive care would lead way for the continuous and proactive healthcare from current sick care and reactive mode.

Adaptive care will have multifaceted delivery modes which can be crafted into different service offering models.

Care a service (Smart homes & Hospitals)

Care as a system (Medical equipment manufacturers)

Care as a Network Data (Insurance companies & Doctors)

Persona Journey

Adaptive care will provide a continuous and effective cover around our daily lifestyle to enable us to avoid and better handle possible ailments and keep us at 100%. Illustration in Fig A shows a futuristic vision of Adaptive care; with adaptive care continuous and proactive tracking of health will be new normal.

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V2X Next wave of transportationtag:www.infosysblogs.com,2018:/infosys-labs//31.107592018-09-21T08:29:04Z2018-09-21T08:41:10ZAdvancement in the societal and new market trends leading to revolution in personal mobility and vehicular transport system. Societal trends such as rapid growth of urbanization putting pressure on current transportation setup, which is growing less compared to the demand,...Pankaj Painuly
Advancement in the societal and new market trends leading to revolution in personal mobility and vehicular transport system. Societal trends such as rapid growth of urbanization putting pressure on current transportation setup, which is growing less compared to the demand, tough emission and energy related regulation are also impacting transportation systems. Apart from this, market trends as advancement of automated driving, real-time and open data accessibility, enabling more effective use of transport assets and also affecting the current transportation systems.]]>
Such trends are enabling a move near to sensitive and intellectual transport infrastructure, with properties of accident-free transportation, supporting higher traffic flow, higher vehicle utilization and more efficient/greener transport. Hence, communication technology such as Vehicle-to everything (V2X) communication, will play a major role to attend these properties.

V2X, means 'vehicle to everything', and it is the part of car communication system. V2X enables autonomous driving with properties as low-latency and high-reliability links. It uses high bandwidth and data flow from sensors and other connected sources.

V2X consists of various components such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-network (V2N) communications. It is a multilayered ecosystem and enables cars to talk with other cars, infrastructure like traffic lights or parking spaces, and to datacenters via cellular networks.

Various car makers and vendors are implementing and experimenting with V2X, V2I, V2V technologies, some of which are highlighted as below.

Qualcomm Technologies collaborated with Panasonic and Ford Motor to launch cellular Vehicle-to-Everything (C-V2X) technologies in Colorado, starting the only U.S. deployment of C-V2X technology.

Audi mounted V2I (provides information based on traffic lights) in few of its Audi A4 models in 2017. It is a traffic management system observing traffic lights using 4G data connection and sending real- time signals to car.

Cadillac CTS sedans launched V2V security technology in US during 2017. It comprises solution of NXP's DSRC and GPS for transmitting and receiving about 1000 messages per second from different vehicles, which are about 300 metres apart. V2X software contains 10 DSRC V2X solution comprising of junction's crash caution, dangerous location notice and emergency vehicle warning.

In Aug, 2017, Hyundai, Kia Started V2X communication solution for a 14-kilometer section of road along with seven junctions in Hwaseong, located in Seoul, Korea for testing autonomous car.

Volkswagen is working on model with IEEE 802.11p-based pWLAN to enable vehicles to communicates with each other. It can help to identify on road situations as, car making emergency stop after its sensors detects obstruction on road.

According to the market research firm MarketsandMarkets, the V2X market is expected to attend CAGR of 17.61% from 2017 to 2025, total market size expected to reach $99.55 Bn by 2025 from $27.19 Bn in 2017. Increasing demand for real-time traffic control, event alerts for increasing public safety, rise in government funds for enhanced traffic management, and the advancement of connected vehicles are some of the leading factors driving the growth of V2X market. Also, growing environmental concerns and extreme competition between car manufacturer are few other factors impacting the growth of V2X.

V2X technology consist of opportunity to transform automotive industry using autonomous cars and help in providing predictive maintenance using real -time monitoring. Though, it possesses challenges of dearth of standardization, lack of acceptance of V2X technology and security of data generated by vehicles.

Upcoming 5G infrastructure expected to provide bandwidth for large amount of connections in a small area enabling individual vehicles to capture more data about their immediate surrounding. V2X technology provide enormous technical and economic advantages compared to other dedicated vehicle connectivity technologies for different stakeholders such as road operators, automakers and mobile operators. It can support wide range of use cases covering safety, navigation and integrated transport systems as compared to different alternatives. 5G cellular system provides advantage of handling all the V2X applications in end-to-end manner using single technology, which makes it scalable and future proof.

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Platformization, the new frontier for IT servicestag:www.infosysblogs.com,2018:/infosys-labs//31.107582018-09-21T08:06:01Z2018-09-21T08:23:38ZJyotirmay RayIt was another busy day at office for Mr. Nayak, at 6 PM it's time to start for home. But, he is not going home today, how can he forgot his anniversary; he specifically set a reminder for it, after the goof up of last year. Mr. Nayak searches for wine & dine restaurants in his smartphone, the app automatically suggests him options for buying flowers and chocolates around the searched location.]]>
The footfall at Malay's Kitchen has reduced to almost one quarter compared to six months ago, while the food rating and customer reviews doesn't show a picture of quality degradation. Couple of miles away from Malay's, few SEZ's started shifting the workforce to other part of city.

The knowledge of location information, location intelligence and "where the customer is now?" is the question that has the potential to serve as a business opportunity in the above two cases. We are entering an era where location based service will gradually transform into platformization of location. The usage of mobile maps by individual users for navigating intra-city, inter-city, finding places, exploring locality etc. has become routine practice. The maturity of the users and appreciation of services has made willingness of majority users to share their location data.

Google is aggressively marketing for Maps services via campaigns such as "LookBeforeYouLeave" or "BikeMode", while maps as a service doesn't generate any revenue, the future of location as platform has immense possibilities. Picture this; google already has profiles of the user having information like "Social-Economic-Personal-Professional" based on the mail, android, mobile usage. Add 24x7 location data of the user to it. Perfect recipe for a platform wherein third parties can leverage the insights for business opportunities. So, when Mr. Mayak searches for a location google already knows that it's his anniversary, and cognition is he might buy flowers, so suggest him best options for flower and chocolate near the searched location even before he looks for. Similarly, Malay's can leverage the location intelligence data to know that his core customers have shifted location. So, either a change in menu or order to delivery mode might be required to keep up the revenue.

Building platforms which are sustainable and appealing to the generation Z users is the key for business to create channels which will be future revenue generators. For example, platformization of location information is evidently going to open-up many such business prospects, wherein third parties or open users can innovate new models for monetization. Platforms with open architecture, developer community, and API network will lead to innovations in product and service development. A healthy ecosystem can grow which will be agile and sustainable.

There are huge opportunities for the IT services in this new wave of platformization. They can shift the role from system integrators to partners in the quest for building and maintaining the platforms. Incumbents should also board the bus of Platformization journey. There are broadly three areas where they could focus a) Developer community& API networks, b) Building Platforms for the clients, c) Product and Services on existing platforms. Tomorrows business needs to be where their customer is and in today's world of platform driven economy what could be better than that.

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Adaptive Inspection: Hurricane Seasontag:www.infosysblogs.com,2018:/infosys-labs//31.107182018-06-29T05:59:58Z2018-06-29T10:59:13ZEnoch Sarin Paul 2017, hurricane Harvey and Irma hit the US coast with
winds exceeding 130 miles per hour, leaving in its wake 103 people dead and an
estimated damage of $200 billion. The double
whammy within 2 months of each other and the severity of the hurricanes is
expected to slow US GDP by 1%.

In Florida alone, the total insured losses were estimated at
more than $5.8 billion, with more than 689,000 residential property claims and
51,396 commercial property claims due to Hurricane Irma. Insurance companies
were inundated with claims and scrambled to process the claims submitted by
their customers. The frenzy was aggravated by the fact that Hurricane Harvey
had hit Texas less than 3 weeks before Hurricane Irma hit Florida.

One of the greatest challenges that Insurers faced during
the 2017 hurricane season was the shortage of adjusters. The first step for
insurers to process claims was to have the adjusters visually assess the damage
and estimate the loss. Unfortunately, most of the adjuster were in Texas
assessing damage due to Hurricane Harvey leading to a shortage of adjusters in
Florida and an increase in adjuster prices in the range of 15% to 25%. The
shortage was only amplified by the lack of access and safety concerns.

Adaptive Inspection technologies which combine the
capabilities of artificial intelligence in the form of computer vision and image
analytics, and edge computing enable insurance companies to leverage autonomous
agents such as drones to inspect property claims more efficiently and
effectively. Drones are capable of flying closer to structures to capture
miniscule details through high resolution images providing a more thorough
report than humans adjusters while reducing the time from 1 hour to 15 minutes.
Edge computing capabilities enable the drones to avoid obstacles, reach the
location and provide images for the image analytics to analyse, estimate damage
and create coverage reports.

his process lays redundant the erstwhile paper based
process resulting in errors, and speeds up the claims process while preventing
adjuster injuries. The technology can also be used to assess property damage in
calamity affected areas before receiving claim requests in order to speed the
process and prevent consumer grief.

Companies like USAA, AIG and Allstate have already deployed
drones to enable adjusters to view hard to reach areas from a safe location and
analyse the images. The technology has rapidly matured over the years and
stands to change the way adjusters and insurance companies assess claims while
changing the way organizations all over the world inspect their physical
assets.

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Blockchain Next: Social Media and AItag:www.infosysblogs.com,2018:/infosys-labs//31.107022018-06-04T10:11:56Z2018-06-04T10:48:05Z The Facebook data breach saga became a global phenomenon with reports suggesting that more than 87 million user profiles were compromised. The company lost over 14% of its market capitalization with #DeleteFacebook trending on Twitter. With over 3 billion...Sounak Sarkar

The
Facebook data breach saga became a global phenomenon with reports suggesting
that more than 87 million user profiles were compromised. The company lost over
14% of its market capitalization with #DeleteFacebook trending on Twitter. With
over 3 billion active users on various social media platforms, this data breach
might just be the tip of the iceberg.

]]>

Companies like Facebook and Google are built on a Surveillance Capitalism business model
business model where they collect all available user information and harvest it so that they could effectively advertise to them. As per some estimates, Facebook controls 25% of the world's social media data and enterprises are leveraging this to create personalized advertisement strategies. This has fueled the social media marketing spend of enterprises which have almost tripled in the last few years.

The personalization models of these social media companies are reliant on the amount of information these companies capture as it is directly proportional to the performance of the AI engine. Google search results, advertisements on Facebook wall, movie recommendations by Netflix and voice assistants like Alexa are all driven by AI technologies and data is an integral part of these AI driven applications. AI applications are able to predict and recognize patterns by processing large quantum of historical data. The accuracy of these predictions are directly related to the amount of data it has been trained on and this leads to a data arms race among corporations. The flip side of these technological developments is the loss of privacy and data security.

This raises the question- Is there a better way to address data privacy issues without compromising on the benefits of AI driven applications?

AI driven applications built on distributed ledger technologies like blockchain could help users reap the benefits of these AI applications without losing control of their personal information. Today the data is owned by select few companies and they in turn sell that information. With distributed ledger technologies, users would be able to encrypt and track their personal information over a distributed network. Companies building AI applications would bid for the anonymized data on the distributed network and the user would get compensated accordingly depending on how valuable the information is for the company. The system would remove middlemen allaying data privacy fears as well as allowing users to earn profits in lieu of data.

Since metadata are not stored on centralized servers, third parties can't use user information for surveillance, tracking or data gathering. Startups like indorse.io and onG.social are providing blockchain based solutions. While indorse.io uses an ethereum blockchain network to validate various skills and qualities of users similar to LinkedIn, onG.social provides a blockchain based social dashboard through which users can distribute their information across various social media platforms.

Social media and AI applications built on blockchain could help create a network which has improved security and privacy offerings apart from allowing users to have better control over their content.

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Facial biometrics going mainstream...tag:www.infosysblogs.com,2018:/infosys-labs//31.106812018-05-03T06:17:00Z2018-05-03T06:23:10Z Recognizing someone by sight has been the building block of human interaction and more importantly has helped conduct commerce through the course of known history. It has helped build trust over time and eased many interactions and transactions. Of...Sounak SarkarRecognizing someone by sight has been the building block of
human interaction and more importantly has helped conduct commerce through the
course of known history. It has helped build trust over time and eased many
interactions and transactions. Of course, humans carry their very own powerful
computer that instantly helps them recognize, recollect, build context and
communicate effectively. In the recent times however, interactions with
machines have increased substantially bringing in the need for many artificial
means to establish identity - mechanisms such as cards, passwords, finger
prints etc. While these have helped to an extent, humans have had to learn new
ways to interact with systems while also opening up potential loop holes for
exploitation. ]]>

Technology advancements in the recent times are helping make
human machine interactions more natural with improvements in touch, voice,
gestures and more. In order to provide seamless experiences focused more on
achieving the objective rather than wrangling with the technology components,
systems will have to evolve and adapt to real-time situations on the ground.
These systems will also have to allay the many security concerns. Where humans could quickly discover if
something is amiss such as a person trying to gain access under duress, systems
will need to combine facial recognition with other biometrics such as emotion
detection, voice, and gait analysis. This will go a long way to secure and
rebuild trust in this increasingly Cyber Physical world.

Technology for Facial recognition has been around since
the 1960s, but it has been largely outside the realm of the common
man especially with usage restricted to government agencies and hi-tech
security companies. It has been mainly used in areas of security ranging from
tackling drug trafficking through airports to identifying criminals. Due to
limitations in technology, early implementations had very limited success with
a number of false positives and failures in settings such as large crowds.

Mainstream consumer implementations started with security
authentication in laptops e.g. in 2008, Lenovo, Toshiba and Asus all launched
laptops with capabilities of facial recognition to unlock the system. Though
they had some security issues (the system needed only 50% match, hence the
laptops could be opened by someone other than the actual owner of the laptop)
with the facial recognition technology, it marked the foray of the technology
into commercial applications. Then Google's Picasa Web Albums and Facebook
rolled out their own facial recognition applications where users were able to identify
faces on the uploaded pictures. Recent examples are exciting especially due to
very advanced technology being used by the likes of Apple Inc. and Samsung. The
Face ID feature in iPhone X allows users to access Apple Pay, Apple store,
iTunes and third party apps which require biometric identification. Even KFC is
experimenting with facial recognition technology in partnership with Baidu in
China. The system installed in one of the KFC outlets in Beijing's financial district recommend menu items based on a
customer's estimated age, mood and gender. In other forms of
implementation, Walmart is also testing
facial recognition applications in their stores where the system would identify
varying levels of dissatisfaction among customers.

The advancements in multi-factor authentication using
biometrics led by Facial recognition augmented with emotions, gestures and
possibly voice are set to revolutionize the payments world. An example from an
outlet of KFC in the Chinese city of Hangzhou, where the company has teamed up
with Alibaba's Ant Financial to launch its facial recognition based payment
system 'Smile to Pay'. Through this
service, customers can make the payment simply by smiling at the self-service
screens. Last year MasterCard launched its 'SelfiePay'
system through which users can verify payments through face verification on the
MasterCard app. Even Amazon has applied for patent for an application which
uses facial recognition to allow payments.

In an endeavor to provide a seamless shopping experience to
customers, experts believe that adoption of such technologies would be the
fastest in the retail and payments industry. As we can see with the examples
above, biometric identification has gone beyond finger prints and it seems that
facial recognition technology is indeed poised for primetime.

]]>
Quantum Computing- The next computing revolutiontag:www.infosysblogs.com,2018:/infosys-labs//31.106802018-05-03T05:50:11Z2018-05-03T06:11:27ZWith increasing levels of data volume, variety and velocity, enterprises are looking for newer avenues to tackle the data problem as well as invent newer security measures. Quantum computer which are inspired by theories of quantum physics, promises to address these data issues along with provide an efficient way to analyze these data. Quantum computers would be able to provide a more sophisticated encryption methodology which are completely different that current methods.Sounak SarkarIn a conference hosted by MIT's Laboratory for Computer
Science in 1981, Richard Feynman proposed the concept of computers which would
harness the strange characteristics of matter at the atomic level to perform
calculations. Last year, IBM open-sourced its quantum computing network called
the IBM Q- Experience to encourage researchers and enterprises to explore
various possibilities of quantum computing. Other companies like Google,
Microsoft and Intel are also in the race to build their own quantum computer to
leverage its exceptional computing capabilities.]]>

Quantum computing leverages quantum physics concepts where
atomic and sub-atomic particles can inhabit multiple, mutually exclusive states
at the same time (called superposition) and are inextricably linked to each
other in perfect unison even if separated by great distance (called
entanglement).

While classical computers usually take one of the two
values, 0 or 1, a quantum computer can be in both 0 and 1 state at the same
time. These fundamental units of information in quantum computing realm are
termed as quantum bits or qubits. Because of these characteristic of being in
two states at the same time, the computational capabilities of quantum computers
increases exponentially.

With an ever increasing number of connected devices, the
volume of data being generated is also increasing and classical computers are
not able to process the whole spectrum of data in an efficient manner. Along
with the growing data volumes, organizations are also struggling with
safeguarding these sensitive and confidential information. While a conventional
computer might take over 2000 years to decrypt the most sophisticated levels of
encryption currently available, a quantum computer would be able to decrypt the
same in a matter of weeks. This has generated growing interest among
enterprises and government organizations to develop new encryption algorithms
which would be withstand brute force attacks from quantum computers. One such
development is the concept of Quantum Key Distribution that uses quantum
technology as a mechanism to ensure data security and privacy. Apart from these,
quantum computers can also help in undertaking various optimization calculations
across industries like airline, manufacturing, retail etc.

Industries like financial services have complex business
processes and are constantly facing data security threats, forcing organizations
like Barclays, Goldman Sachs, J.P.Morgan to search for new alternatives. They
have now become some of the early adopters of quantum computing technologies
and are undertaking various experimentations in the field of portfolio
optimization, fraud detection and data security. Apart from financial services
organizations, other companies like Airbus and Volkswagen are also
experimenting with quantum computers in the field of product design and supply
chain optimization respectively.

Even though current applications of quantum computers are
limited to solving intractable problem in areas of optimization, sampling and
machine learning, further development in hardware and software technologies would
enable quantum computers to solve global problems like food scarcity, weather
pattern detection and resource optimization.

Market trends suggest that quantum computing has reached an
inflection point- moving from theoretical research to commercial
implementations. Even though an enterprise worthy quantum computer having
computing capabilities of atleast 50 qubits is still 3-5 years away, companies
have to rethink their strategies in line with these developments and draw out
future roadmap accordingly.

]]>
Cognitive System-Mimicking Human Understandingtag:www.infosysblogs.com,2018:/infosys-labs//31.106602018-03-29T09:04:12Z2018-03-29T09:55:22Z With advancements in artificial intelligence algorithms, it's possible for machines to mimic human understanding. They are able to analyze and interpret information, make deductions and identify patterns from the information sets analogous to human brain. These new generation of...Sounak SarkarWith advancements in artificial intelligence algorithms, it's
possible for machines to mimic human understanding. They are able to analyze
and interpret information, make deductions and identify patterns from the
information sets analogous to human brain. These new generation of machines are
categorized as cognitive systems. These systems aggregate machine intelligence,
predictive analytics, machines learning, natural language engines and image/video/text
analytics to enhance human-machine interaction.]]>
Evolution of Cognitive System

The evolution of cognitive systems can be classified into
three types:

Cognitive Systems for Process Automation

Cognitive System for deriving Insights

Cognitive System for Engagement

Cognitive Systems for
Process Automation

The first phase focusses on the various machine learning and
robotic process automation applications to develop substantial domain insights
of particular processes and aim to automate them. This phase is aimed at
automating repetitive, mundane and low intelligent jobs that employs highly
trained human manpower. An example of it is the character recognition and
handwriting detection tools deployed by various banks and financial
institutions in middle and back office operations to reduce risk and cost.
Another example is implementation of chatbots in customer service fields where
the bot is able to answer general customer enquires with regards to account
balance, credit card offers, utility bill payment questions etc. Cognitive
automation helps in improving efficiency as greater volume of data can be
processed at a faster rate while improving the compliance capabilities and
reducing errors.

Cognitive Systems for
deriving Insights

This phase of cognitive evolution encompasses extraction of
meaningful insights and relationships from a myriad of data streams comprising
of both structured and unstructured data. This phase is evolutionary in nature
as the accuracy of the insights and observations improves as the system
processes increased amount of data. Cognitive insights have capabilities of
providing actionable understandings into possible future events by sensing and
analyzing past and present events. This would enable leaders to plan and
prioritize future strategies and roadmaps, and augment enterprise capabilities
with changing market dynamics.An
example of cognitive insights could be implementing deep learning networks to
understand credit card usage patterns among working class customers aged
between 25 years and 40 years. Such tailored and actionable insight would help
the bank to create hyper-personalized offerings which would be beneficial for
the customers and in turn would improve customer loyalty.

Cognitive Systems for
Engagement

The final phase of cognitive evolution is intelligent agents
that interact and engage with customers using cognitive capabilities. An example
of cognitive engagement could be the deployment of voice assisted virtual
agents (like Alexa, Siri etc.) to interact with human for performing certain
specific tasks. Customers can book an appointment with a fund manager through
an Alexa enabled interface or employees' can clarify doubts with regards to the
HR policies in an organization through a voice enabled assistant. Cognitive
systems have the capabilities to unlock the power of unstructured information
flowing through various digital engagement channels and other sources,
leveraging image/ video or text analytics to generate actionable insights and
helping the bank to develop personalized relationship with the customer.

Characteristics of Cognitive System

Cognitive systems are characterized by their capabilities to
understand and extract context from data and learn from patterns to generate
future predictions. Some of the characteristics of cognitive systems are:

Identifying Contexts: Cognitive Systems are capable of contextualizing information extracted from various data sources and deduce understanding based on context. As such, these systems are able to process information that are situation aware and build suitable data relationship models.

Decision Making: Because of its capabilities to identify and establish context, cognitive systems are able to reason and enable decision making based on real-time environment variables.

Learn and Improvise: cognitive systems are designed to continuously learn from data inputs and improvise its decision making capabilities based on previous results and feedback received.

Conclusion

Cognitive systems have the potential to help enterprises to
optimize various business processes, infer insights from seemingly
inconsequential unstructured data sets and create personalized engagement
models. Even though the field of cognitive system is ever-evolving, enterprises
should undertake a strategic view point over the long term benefits which could
help the organizations to maintain their competitive advantage.